Linganisha mbinu

	Utambuzi wa Majina ya Entititi za Multimodal	Transformeri wa Multimodal
Nyanja	Ujifunzaji wa Kina	Ujifunzaji wa Kina
Familia	Machine learning	Machine learning
Mwaka wa asili	2018	2019–2021
Mwanzilishi	Moon, S.; Lu, D. et al.	Lu et al. (ViLBERT); Radford et al. (CLIP)
Aina	Sequence labeling with multimodal fusion	Cross-modal attention-based deep learning model
Chanzo asilia	Moon, S., Neves, L., & Carvalho, V. (2018). Multimodal Named Entity Recognition for Short Social Media Posts. Proceedings of NAACL-HLT 2018, pp. 852–860. Association for Computational Linguistics.	Lu, J., Batra, D., Parikh, D., & Lee, S. (2019). ViLBERT: Pretraining Task-Agnostic Visiolinguistic Representations for Vision-and-Language Tasks. Advances in Neural Information Processing Systems (NeurIPS), 32.
Majina mbadala	Multimodal NER, MNER, Visual NER, Cross-modal Named Entity Recognition	multimodal attention model, cross-modal transformer, vision-language transformer, multi-modal fusion transformer
Zinazohusiana	6	5
Muhtasari	Multimodal Named Entity Recognition (MNER) extends classical NER by fusing textual sequences with complementary modalities — most commonly images — to improve the identification and classification of named entities such as persons, organizations, and locations in settings where visual context disambiguates ambiguous or sparse text.	A Multimodal Transformer extends the standard Transformer architecture to process and jointly reason over two or more input modalities — most commonly text and images, but also audio, video, or structured data. Cross-modal attention layers allow information from one modality to inform representations in another, enabling tasks such as visual question answering, image captioning, and multimodal sentiment analysis.